Ocular recognition can utilize periocular images (e.g., images of an iris and surrounding ocular regions of the eye) to identify a subject (e.g., person). For example, known periocular images (e.g., target images stored in a database) can be compared (e.g., matched) to an unknown periocular image (e.g., a probe image) to determine if the probe image has the same subject as any of the target images. For instance, the target images can be compared to the probe image to determine whether the iris in the probe image matches any of the irises in the target images, thereby identifying the subject in the probe image.
The matching of the probe image and the target images can be effectively performed utilizing various processes (e.g., algorithms) when the probe and target images are captured using the same sensor. However, such processes may not be able to effectively match the probe image and the target images, and accordingly may not be able to effectively (e.g., accurately) determine whether the iris in the probe image matches any of the irises in the target images, when the probe and target images are captured using different sensors (e.g., different types of sensors). For example, such processes may not be able to effectively match a probe image and a target image when one of the images is captured using a close range sensor (e.g., a sensor configured to capture an image of a subject that is less than one meter from the sensor) and the other image is captured using a far range sensor (e.g., a sensor configured to capture an image of a subject that is one to ten meters from the sensor).